Book Image

Graph Data Science with Neo4j

By : Estelle Scifo
5 (1)
Book Image

Graph Data Science with Neo4j

5 (1)
By: Estelle Scifo

Overview of this book

Neo4j, along with its Graph Data Science (GDS) library, is a complete solution to store, query, and analyze graph data. As graph databases are getting more popular among developers, data scientists are likely to face such databases in their career, making it an indispensable skill to work with graph algorithms for extracting context information and improving the overall model prediction performance. Data scientists working with Python will be able to put their knowledge to work with this practical guide to Neo4j and the GDS library that offers step-by-step explanations of essential concepts and practical instructions for implementing data science techniques on graph data using the latest Neo4j version 5 and its associated libraries. You’ll start by querying Neo4j with Cypher and learn how to characterize graph datasets. As you get the hang of running graph algorithms on graph data stored into Neo4j, you’ll understand the new and advanced capabilities of the GDS library that enable you to make predictions and write data science pipelines. Using the newly released GDSL Python driver, you’ll be able to integrate graph algorithms into your ML pipeline. By the end of this book, you’ll be able to take advantage of the relationships in your dataset to improve your current model and make other types of elaborate predictions.
Table of Contents (16 chapters)
1
Part 1 – Creating Graph Data in Neo4j
4
Part 2 – Exploring and Characterizing Graph Data with Neo4j
8
Part 3 – Making Predictions on a Graph

Importing CSV data into Neo4j with Cypher

The comma-separated values (CSV) file format is the most widely used to share data among data scientists. According to the dataset of Kaggle datasets (https://www.kaggle.com/datasets/morriswongch/kaggle-datasets), this format represents more than 57% of all datasets in this repository, while JSON files account for less than 10%. It is popular for the following reasons:

  • How it resembles the tabular data storage format (relational databases)
  • Its closeness to the machine learning world of vectors and matrices
  • Its readability – you usually just have to read column names to understand what it is about (of course, a more detailed description is required to understand how the data was collected, the unit of physical quantities, and so on) and there are no hidden fields (compared to JSON, where you can only have a key existing from the 1,000th record and later, which is hard to know without a proper description or advanced data...